Efficiency of volatile baited sticky traps for the Tarnished Plant Bug (<i>Lygus lineolaris</i>) in strawberry fields
Bibliographic record
Abstract
Abstract Insecticides have adverse effects on human health and the environment. Thus, the development of non‐chemical replacement to manage insect pests is urgent. An alternative is to bait sticky traps with attractive insect sex pheromones or plant volatiles. In Quebec, the tarnished plant bug (Lygus lineolaris) is a major insect pest. We tested the efficiency of sex pheromones (mixture of hexyl butyrate (HB), (E)‐2‐hexenyl butyrate (E2HB) and (E)‐4‐oxo‐2‐hexenal (KA)) and sunflower (Helianthus annuus) volatiles (pinene, sabinene and phenylacetaldehyde) as sticky trap baits for the tarnished plant bug in strawberry fields of the Laurentians in Southern Québec, Canada. The pheromones decreased the number of tarnished plant bug caught in the traps compared to a control. The sunflower volatile did not have any effect on the number of individuals caught in the traps. Different Lygus species use HB, E2HB and KA in different ratios for sexual signals and alarm signals and for species recognition. GC‐MS analysis of the sex pheromone bait revealed that the ratios between the three main compounds did not match the intended ratio for the L. lineolaris species. This mismatch probably explains our results. Individuals were not attracted to sunflower volatiles. Our results point to the difficulty of manufacturing and using sex pheromones as baits. Future work should assess the effect of several pheromone ratios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".